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I got a number of data like:
a0:86:c6:52:4e:e8,0.006568,0.006620,Out
a0:86:c6:52:4e:e8,0.006663,0.006695,In
a0:86:c6:52:4e:e8,0.008089,0.008141,Out
a0:86:c6:52:4e:e8,0.008185,0.008217,In
01:00:5e:00:00:fb,0.033096,0.035016,Out
33:33:00:00:00:fb,0.034997,0.037077,Out
01:00:5e:7f:ff:fa,0.039969,0.042057,Out
ff:ff:ff:ff:ff:ff,0.059823,0.061639,Out
a0:86:c6:52:4e:e8,0.068865,0.068917,Out
a0:86:c6:52:4e:e8,0.068962,0.068994,In
a0:86:c6:52:4e:e8,0.083492,0.083544,Out
a0:86:c6:52:4e:e8,0.083588,0.083620,In
...
Actually, it lasts for 120 seconds. And I want to plot these data to something like a gantt graph using matplotlib.
The reason is that most gantt chart api only support time format YY-MM-DD HH:MM:SS, and not in so small (microsecond) size.
Please tell me how to do this?
564
asked Apr 12 '17 10:04
MatPlotLib with Python It's a type of bar chart that lists the tasks on the vertical axis and the time intervals on the horizontal axis. The width of the horizontal bars in the graph shows the duration of each activity. To plot a Gantt chart in matplotlib, we can use the broken_barh() method.
If you are using Matplotlib from within a script, the function plt. show() is your friend. plt. show() starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.
Plotly has several advantages over matplotlib. One of the main advantages is that only a few lines of codes are necessary to create aesthetically pleasing, interactive plots. The interactivity also offers a number of advantages over static matplotlib plots: Saves time when initially exploring your dataset.
To create a real-time plot, we need to use the animation module in matplotlib. We set up the figure and axes in the usual way, but we draw directly to the axes, ax , when we want to create a new frame in the animation.
In order to produce a Gantt chart in matplotlib, one may use the plt.broken_barh function. There is a broken_barh example on the matplotlib page.
In order to filter and group the data, one can use pandas, especially the groupby function of a dataframe.
Here is a complete example:
inp = u"""a0:86:c6:52:4e:e8,0.006568,0.006620,Out
a0:86:c6:52:4e:e8,0.006663,0.006695,In
a0:86:c6:52:4e:e8,0.008089,0.008141,Out
a0:86:c6:52:4e:e8,0.008185,0.008217,In
01:00:5e:00:00:fb,0.033096,0.035016,Out
33:33:00:00:00:fb,0.034997,0.037077,Out
01:00:5e:7f:ff:fa,0.039969,0.042057,Out
ff:ff:ff:ff:ff:ff,0.059823,0.061639,Out
a0:86:c6:52:4e:e8,0.068865,0.068917,Out
a0:86:c6:52:4e:e8,0.068962,0.068994,In
a0:86:c6:52:4e:e8,0.083492,0.083544,Out
a0:86:c6:52:4e:e8,0.083588,0.083620,In"""
import pandas as pd
import io
import matplotlib.pyplot as plt
df = pd.read_csv(io.StringIO(inp), header=None, names=["Task", "Start", "Finish", "Resource"] )
df["Diff"] = df.Finish - df.Start
color = {"In":"turquoise", "Out":"crimson"}
fig,ax=plt.subplots(figsize=(6,3))
labels=[]
for i, task in enumerate(df.groupby("Task")):
labels.append(task[0])
for r in task[1].groupby("Resource"):
data = r[1][["Start", "Diff"]]
ax.broken_barh(data.values, (i-0.4,0.8), color=color[r[0]] )
ax.set_yticks(range(len(labels)))
ax.set_yticklabels(labels)
ax.set_xlabel("time [ms]")
plt.tight_layout()
plt.show()
which produces the following plot:
90
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